Journal: Molecular cancer research : MCR

Article Title: Gene body methylation of the lymphocyte-specific gene CARD11 results in its overexpression and regulates cancer mTOR signaling

doi: 10.1158/1541-7786.MCR-20-0753

Figure Lengend Snippet: (A) Heat map of all high confidence cancer-related genes with a correlation coefficient of >0.5 between gene body methylation and gene expression are shaded red across 32 cancer types. (B) Association between CARD11 expression and overall survival in kidney renal cell carcinoma patients. (C) Association between CARD11 gene body methylation and overall survival in kidney renal cell carcinoma patients. (D) CARD11 immunohistochemical staining in representative kidney renal cell carcinoma sample showing high expression in epithelial cells. (Scale bar = 100 μm)

Article Snippet: The change in methylation between normal kidney tissue and renal cell carcinoma in TCGA samples was then evaluated and a significant increase in methylation in renal cell carcinoma was observed in each of the five sites interrogated in this region ( Fig. 3D ). (A) Correlation of CARD11 gene body DNA methylation and gene expression in KIRC and LUAD. (B-C) Effect of DNA methylation inhibition on CARD11 expression. (D) Change in methylation between normal kidney tissue and renal cell carcinoma in CARD11 gene body CpG island associated methylation sites. (E) CARD11 gene body methylation analysis by pyrosequencing. (F) CARD11 gene body methylation analysis by PCR. (G) Effect of gene body demethylation on CARD11 gene expression.

Techniques: Methylation, Gene Expression, Expressing, Immunohistochemical staining, Staining

Journal: Molecular cancer research : MCR

Article Title: Gene body methylation of the lymphocyte-specific gene CARD11 results in its overexpression and regulates cancer mTOR signaling

doi: 10.1158/1541-7786.MCR-20-0753

Figure Lengend Snippet: (A) Pathways significantly downregulated upon CARD11 knockdown in H1975 cells. (B) CARD11 knockdown inhibits S6 phosphorylation in H1975 and UOK111 cells. (C) Impact of CARD11 knockdown on colony formation. (D) Effect of CARD11 knockdown on LC3 lipidation (BAF = bafilomycin A1). (E) Impact of CARD11 knockdown on autophagic flux in GFP-RFP-LC3 expressing cells. (F) Effect of CARD11 knockdown on autophagic flux quantification. Statistics were performed using unpaired t-tests for comparisons between two groups and one-way ANOVA with Tukey’s post-test for multiple comparisons or Two-way ANOVA for more than 2 groups. Statistical values were considered significant when p < 0.05 (*p < 0.05, **p < 0.01, ***p < 0.001). (Scale bar = 20 μm)

Article Snippet: The change in methylation between normal kidney tissue and renal cell carcinoma in TCGA samples was then evaluated and a significant increase in methylation in renal cell carcinoma was observed in each of the five sites interrogated in this region ( Fig. 3D ). (A) Correlation of CARD11 gene body DNA methylation and gene expression in KIRC and LUAD. (B-C) Effect of DNA methylation inhibition on CARD11 expression. (D) Change in methylation between normal kidney tissue and renal cell carcinoma in CARD11 gene body CpG island associated methylation sites. (E) CARD11 gene body methylation analysis by pyrosequencing. (F) CARD11 gene body methylation analysis by PCR. (G) Effect of gene body demethylation on CARD11 gene expression.

Techniques: Knockdown, Phospho-proteomics, Expressing

Journal: Molecular cancer research : MCR

Article Title: Gene body methylation of the lymphocyte-specific gene CARD11 results in its overexpression and regulates cancer mTOR signaling

doi: 10.1158/1541-7786.MCR-20-0753

Figure Lengend Snippet: (A) Correlation of CARD11 gene body DNA methylation and gene expression in KIRC and LUAD. (B-C) Effect of DNA methylation inhibition on CARD11 expression. (D) Change in methylation between normal kidney tissue and renal cell carcinoma in CARD11 gene body CpG island associated methylation sites. (E) CARD11 gene body methylation analysis by pyrosequencing. (F) CARD11 gene body methylation analysis by PCR. (G) Effect of gene body demethylation on CARD11 gene expression. Statistics were performed using unpaired t-tests for comparisons between two groups and one-way ANOVA with Tukey’s post-test for multiple comparisons for more than 2 groups. Statistical values were considered significant when p < 0.05 (*p < 0.05, **p < 0.01, ***p < 0.001).

Article Snippet: The change in methylation between normal kidney tissue and renal cell carcinoma in TCGA samples was then evaluated and a significant increase in methylation in renal cell carcinoma was observed in each of the five sites interrogated in this region ( Fig. 3D ). (A) Correlation of CARD11 gene body DNA methylation and gene expression in KIRC and LUAD. (B-C) Effect of DNA methylation inhibition on CARD11 expression. (D) Change in methylation between normal kidney tissue and renal cell carcinoma in CARD11 gene body CpG island associated methylation sites. (E) CARD11 gene body methylation analysis by pyrosequencing. (F) CARD11 gene body methylation analysis by PCR. (G) Effect of gene body demethylation on CARD11 gene expression.

Techniques: Methylation, Expressing, DNA Methylation Assay, Gene Expression, Inhibition

Journal: Molecular cancer research : MCR

Article Title: Gene body methylation of the lymphocyte-specific gene CARD11 results in its overexpression and regulates cancer mTOR signaling

doi: 10.1158/1541-7786.MCR-20-0753

Figure Lengend Snippet: (A) Doxycycline-induced CARD11 overexpression in A498 cells. (B) Effect of mTOR inhibition on CARD11 induced S6 phosphorylation. (C-D) Tumor volume and tumor weight of doxycycline-induced subcutaneous A498 tumor model. (E-F) Tumor volume and tumor weight of subcutaneous Caki tumor model. Statistics were performed using unpaired t-tests for comparisons between two group or two-way ANOVA for more than 2 groups. Statistical values were considered significant when p < 0.05 (*p < 0.05).

Article Snippet: The change in methylation between normal kidney tissue and renal cell carcinoma in TCGA samples was then evaluated and a significant increase in methylation in renal cell carcinoma was observed in each of the five sites interrogated in this region ( Fig. 3D ). (A) Correlation of CARD11 gene body DNA methylation and gene expression in KIRC and LUAD. (B-C) Effect of DNA methylation inhibition on CARD11 expression. (D) Change in methylation between normal kidney tissue and renal cell carcinoma in CARD11 gene body CpG island associated methylation sites. (E) CARD11 gene body methylation analysis by pyrosequencing. (F) CARD11 gene body methylation analysis by PCR. (G) Effect of gene body demethylation on CARD11 gene expression.

Techniques: Over Expression, Inhibition, Phospho-proteomics

Journal: Gastroenterology and Hepatology From Bed to Bench

Article Title: The epigenetic influence of diet-induced gut microbiome changes in precision nutrition – a systematic review

doi: 10.22037/ghfbb.v18i3.3136

Figure Lengend Snippet: Epigenetic Changes in Gene Expression — This figure illustrates two key epigenetic mechanisms that regulate gene expression: DNA methylation and histone modifications. DNA methylation involves the addition of methyl groups to the promoter region of a gene, inhibiting transcription and leading to gene silencing. Histone modifications, including acetylation and methylation, alter chromatin structure and gene accessibility; acetylation relaxes chromatin, promoting gene activation, while methylation can either enhance or suppress transcription depending on its context. These epigenetic modifications are dynamic and influenced by environmental factors, such as diet, with implications for metabolism, inflammation, and disease susceptibility.

Article Snippet: Sun, et al. (21) , DNA methylation analysis (DREAM sequencing, bisulfite pyrosequencing, qRT-PCR) in a microbiota-inflammation model. , Difficulty separating inflammation’s role in epigenetic changes. , Microbiota and inflammation influenced DNA methylation at CpG sites. , DNA methylation at CpG sites. , Not assessed. , Multi-model approach, deep-sequencing of methylation changes. , Moderate , 2023.

Techniques: Gene Expression, DNA Methylation Assay, Methylation, Activation Assay

Journal: Gastroenterology and Hepatology From Bed to Bench

Article Title: The epigenetic influence of diet-induced gut microbiome changes in precision nutrition – a systematic review

doi: 10.22037/ghfbb.v18i3.3136

Figure Lengend Snippet: Epigenetic Modifications in Metabolic Health and Disease Prevention – This figure illustrates the impact of diet-induced changes in gut microbiome composition on epigenetic modifications and their subsequent effects on metabolic health. The dietary components, such as fiber-rich, polyphenol-rich, and high-fat diets, are shown to influence microbiome diversity and promote the production of metabolites like short-chain fatty acids (SCFAs), which modulate epigenetic markers (e.g., DNA methylation, histone modifications) and gene expression related to inflammation, metabolism, and disease susceptibility. Diets rich in fiber and polyphenols are associated with beneficial microbiome shifts and favorable epigenetic modifications that support metabolic health and reduce the risk of metabolic diseases like obesity and insulin resistance. In contrast, Western-style diets high in fat and processed foods contribute to dysbiosis, inflammation, and adverse epigenetic changes, which may increase the risk of developing chronic metabolic disorders, including obesity and type 2 diabetes.

Article Snippet: Sun, et al. (21) , DNA methylation analysis (DREAM sequencing, bisulfite pyrosequencing, qRT-PCR) in a microbiota-inflammation model. , Difficulty separating inflammation’s role in epigenetic changes. , Microbiota and inflammation influenced DNA methylation at CpG sites. , DNA methylation at CpG sites. , Not assessed. , Multi-model approach, deep-sequencing of methylation changes. , Moderate , 2023.

Techniques: DNA Methylation Assay, Gene Expression, Western Blot

Journal: International Journal of Molecular Sciences

Article Title: Molecular Implications of ADIPOQ, GAS5, GATA4 , and YAP1 Methylation in Triple-Negative Breast Cancer Prognosis

doi: 10.3390/ijms262110652

Figure Lengend Snippet: Ki-67 stratified by methylation status of ADIPOQ ( a ), GATA-4 ( b ), and YAP1 ( c ) in the tumor tissue. For the ADIPOQ gene ( a ), the “unmethylated” group was combined with the “partially methylated” group due to the presence of only one sample with an unmethylated status. For the YAP-1 gene ( c ), the “partially methylated” group was combined with the “methylated” group due to the presence of only two samples with methylated status. Data are shown as raw values with medians and IQR. Group differences were analyzed with Student’s t -test ( c ), Mann–Whitney U-test ( a ), and one-way ANOVA ( b ).

Article Snippet: Future studies should include larger, prospective cohorts using quantitative methylation assays (e.g., pyrosequencing) and integrate matched gene expression data to link methylation status to biological outcomes functionally.

Techniques: Methylation, MANN-WHITNEY

Journal: International Journal of Molecular Sciences

Article Title: Molecular Implications of ADIPOQ, GAS5, GATA4 , and YAP1 Methylation in Triple-Negative Breast Cancer Prognosis

doi: 10.3390/ijms262110652

Figure Lengend Snippet: Age at diagnosis stratified by methylation status of ADIPOQ ( a ), GATA-4 ( b ), and YAP1 ( c ). For the ADIPOQ gene ( a ), the “unmethylated” group was combined with the “partially methylated” group due to the presence of only one sample with an unmethylated status. For the YAP-1 gene ( c ), the “partially methylated” group was combined with the “methylated” group due to the presence of only two samples with methylated status. Data are shown as raw values with medians and IQR. Group differences were analyzed with Student’s t -test ( a , c ) and Kruskal–Wallis test ( b ).

Article Snippet: Future studies should include larger, prospective cohorts using quantitative methylation assays (e.g., pyrosequencing) and integrate matched gene expression data to link methylation status to biological outcomes functionally.

Techniques: Biomarker Discovery, Methylation

Journal: International Journal of Molecular Sciences

Article Title: Molecular Implications of ADIPOQ, GAS5, GATA4 , and YAP1 Methylation in Triple-Negative Breast Cancer Prognosis

doi: 10.3390/ijms262110652

Figure Lengend Snippet: ADIPOQ methylation as having the most significant associations, being associated with all five survival endpoints in TCGA TNBC cohort, including ( a ) DSS ( p = 0.023), ( b ) DFI ( p = 0.013), ( c ) PFI ( p = 0.037), ( d ) RFS ( p = 0.011) and ( e ) OS ( p = 0.028).

Article Snippet: Future studies should include larger, prospective cohorts using quantitative methylation assays (e.g., pyrosequencing) and integrate matched gene expression data to link methylation status to biological outcomes functionally.

Techniques: Methylation

Journal: BMC Psychiatry

Article Title: Overweight and POMC methylation: epigenetic associations with adolescent depression

doi: 10.1186/s12888-025-07162-y

Figure Lengend Snippet: Comparison of POMC gene promoter region methylation level among depression with weight loss group, depression with weight normal BMI group, and depression with overweight group. Bold values: P < 0.05. Abbreviations: CpG Cytosine-phosphate-Guanine, CpG1-8 CpG island methylation site 1–8

Article Snippet: Pyrosequencing methylation analysis was used to assess POMC gene promoter methylation levels

Techniques: Comparison, Methylation

Journal: BMC Psychiatry

Article Title: Overweight and POMC methylation: epigenetic associations with adolescent depression

doi: 10.1186/s12888-025-07162-y

Figure Lengend Snippet: Spearman correlation analysis between POMC gene promoter methylation levels and psychometric scores. ( A ) Depression with weight normal BMI group; ( B ) Depression with overweight group; ( C ) Depression with weight loss group

Article Snippet: Pyrosequencing methylation analysis was used to assess POMC gene promoter methylation levels

Techniques: Methylation